GeoVista: Web-augmented Agentic Visual Reasoning

Agentic visual reasoning has advanced multimodal understanding through multi-step tool use, yet current methods restrict these tools to image manipulation and never reach for external knowledge, leaving open-source models brittle on tasks that require both. We study this setting through geolocalization, a task that inherently couples fine-grained visual inspection with open-world knowledge retrieval. We introduce \textbf{GeoVista}, a 7B agentic model that interleaves image zoom-in and web-search tool calls within a reasoning loop, and, to move beyond the low resolution of prior geolocalization benchmarks, curate \textbf{GeoBench}, a benchmark of high-resolution photos, panoramas, and satellite imagery whose geographic cues are only resolvable through fine-grained visual search. We train GeoVista in two stages: a cold-start SFT on synthesized agentic trajectories to instill the tool-use format, followed by RL with \textbf{CasPO} (Cascaded Policy Optimization). Instead of treating each prediction as a pass-or-fail event, CasPO cascades credit along the country, province, and city hierarchy, yielding a dense, multi-scale learning signal. Beyond accuracy, CasPO reshapes reasoning behavior: fewer tool-call errors, more targeted searches, and emergent self-reflection. GeoVista achieves state-of-the-art among open-source models and matches proprietary systems such as Gemini-2.5-flash and GPT-5 across most metrics, despite being an order of magnitude smaller.